{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 引入必要的库包\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         date  电力可供量(亿千瓦小时)  电力生产量(亿千瓦小时)  水电生产电力量(亿千瓦小时)  火电生产电力量(亿千瓦小时)  \\\n",
      "0  2004-01-01       21972.3       22033.1          3535.4         17955.9   \n",
      "1  2005-01-01       24940.8       25002.6          3970.2         20473.4   \n",
      "2  2006-01-01       28588.4       28657.3          4357.9         23696.0   \n",
      "3  2007-01-01       32712.4       32815.5          4852.6         27229.3   \n",
      "4  2008-01-01       34540.8       34668.8          5851.9         27900.8   \n",
      "\n",
      "   核电生产电力量(亿千瓦小时)  风电生产电力量(亿千瓦小时)  电力进口量(亿千瓦小时)  电力出口量(亿千瓦小时)  \\\n",
      "0           504.7           446.2          34.0          94.8   \n",
      "1           530.9           446.2          50.1         111.9   \n",
      "2           548.4           446.2          53.9         122.7   \n",
      "3           621.3           446.2          42.5         145.7   \n",
      "4           683.9           446.2          38.4         166.4   \n",
      "\n",
      "   电力能源消费总量(亿千瓦小时)  农、林、牧、渔业电力消费总量(亿千瓦小时)  工业电力消费总量(亿千瓦小时)  建筑业电力消费总量(亿千瓦小时)  \\\n",
      "0          21971.4                  768.9          16424.3             202.1   \n",
      "1          24940.3                  776.3          18521.7             233.9   \n",
      "2          28588.0                  827.0          21267.7             271.0   \n",
      "3          32711.8                  879.0          24290.8             309.0   \n",
      "4          34541.4                  887.1          25388.6             367.3   \n",
      "\n",
      "   交通运输、仓储和邮政业电力消费总量(亿千瓦小时)  批发和零售业、住宿和餐饮业电力消费总量(亿千瓦小时)  其他电力消费总量(亿千瓦小时)  \\\n",
      "0                     449.6                       705.4           1036.6   \n",
      "1                     430.3                       752.3           1340.9   \n",
      "2                     467.4                       847.3           1555.9   \n",
      "3                     531.9                       929.8           1708.6   \n",
      "4                     571.8                      1017.4           1913.0   \n",
      "\n",
      "   居民生活电力消费总量(亿千瓦小时)  电力终端消费量(亿千瓦小时)  工业终端电力消费量(亿千瓦小时)  输配电损失量(亿千瓦小时)  \n",
      "0             2384.5         20550.8           15003.7         1420.6  \n",
      "1             2884.8         23233.8           16815.2         1706.5  \n",
      "2             3351.6         26729.1           19408.9         1858.8  \n",
      "3             4062.7         30650.1           22229.1         2061.7  \n",
      "4             4396.1         32403.5           23250.8         2137.9  \n"
     ]
    }
   ],
   "source": [
    "# 读取数据\n",
    "data = pd.read_csv('../ClearData/Electricity_20_bfill_date.csv', encoding='utf-8')\n",
    "# 打印出数据的前5行，检查数据是否导入正确\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 17624717.537499998\n"
     ]
    }
   ],
   "source": [
    "# 导入所需的库\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import numpy as np\n",
    "# 选择特征和标签\n",
    "X = data[[\"火电生产电力量(亿千瓦小时)\",\"水电生产电力量(亿千瓦小时)\",\"核电生产电力量(亿千瓦小时)\"]]\n",
    "y = data[\"电力生产量(亿千瓦小时)\"]\n",
    "\n",
    "# 将数据集分为训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "# 创建决策树回归模型\n",
    "regressor = DecisionTreeRegressor(random_state=0)\n",
    "\n",
    "# 训练模型\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# 使用训练好的模型进行预测\n",
    "y_pred = regressor.predict(X_test)\n",
    "\n",
    "# 评估模型性能\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "\n",
    "# 如果需要，可以打印出决策树的规则\n",
    "# from sklearn.tree import export_text\n",
    "# r = export_text(regressor)\n",
    "# print(r)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出预测值和真实值的对比图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(y_test.values, label=\"Actual\")\n",
    "plt.plot(y_pred, label=\"Predicted\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PL",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
